JOURNAL
Detection
OF GEOPHYSICAL
RESEARCH,
and characterization
VOL. 106, NO. D24, PAGES 33,405-33,419, DECEMBER
of boreal
coniferous
27, 2001
forests
from remote sensing data
Jean-LucWidlowski, Bernard Pinty, Nadine Gobron, and Michel M. Verstraete
SpaceApplicationsInstitute of the EC Joint ResearchCentre, Ispra (VA), Italy
Abstract. Advancedradiationtransfermodelscapableof representingthe reflectanceof
the coupledsurfaceand atmospheresystemhave been usedto generatelook-up tablesof
simulatedremote sensingmeasurementsat the top of the atmospherefor typical
conditionsof forestcoverand atmosphericcompositionfound in northernEurope. These
simulationswere evaluatedagainstactual observationsunder identicalviewing and
illuminationgeometries,availablefor the blue, red, and near-infraredspectralbandsof
the VEGETATION instrumentto retrieve the most likely of a set of predefinedsolutions
to the inverseproblem.The accumulationof resultsover multiple daysin the summerof
1999 permitted the establishmentof mapsshowingthe likelihoodof identifyingthe
predefinedforest types,their correspondingstructuralcharacteristics,
aswell as the
associatedatmosphericopticaldepth on the day of retrieval.The proposedmethodologyis
completelygenericand thus can easilybe prototypedfor different biomesand
instruments.
1.
Introduction
Current ecological and climatological research interests
place considerableemphasison the magnitudeand dynamics
of the physicaland chemicalprocessesthat control the exchangesof water, energy,and carbonat the interface between
the biosphereand the atmosphere.Accurateknowledgeof the
spatialand temporalvariabilityof the world'sforest characteristicsis not only mandatoryto verify the implementationof
internationaltreaties(climatechange,environmentaldegradation, biodiversitypreservation,etc.) but alsoto provideappropriate initial and boundaryconditionsfor general circulation
or global climate models[e.g.,Knorr et al. 1995].
In particular,the boreal ecosystem,which coversextensive
and often inaccessibleareas,has become the target of an intensifiedeffort to quantifythe whereaboutsof the missingCO2
in the context of the global carbon budget. Satellite remote
sensing,in principle, can provide a convenient,efficient, and
cost-effectiveway to gather this information,becausedata can
be acquiredrepetitivelyover large areasand at a spatialresolution adequateto addressmany key ecologicaland climate
changerelatedissues[l/erstraete,
1994].A varietyof land-cover
informationextractionschemeshavethusbeen appliedto satellite-gatheredmeasurements
in the past.These include(unsupervised)supervisedimage classification
techniquesof single, multitemporaland/or multisensordata, or metricsderived
thereof,aswell asspectralunmixing,theuseof expertknowledge,
simplespectralvegetationindices,and a wide varietyof ancillary
validationdata[Gervinetal., 1985;Justice
etal., 1985;Tuckeretal.,
spheric,topographic,and seasonaldisturbances;
othersneglect
the reflectanceanisotropyof natural surfaces,the influenceof
the canopystructureand backgroundon the satellite-gathered
data stringsor even saturation problems of the vegetation
indicatoritself (for a documentationof these effects,see for
example,Baretand Guyot[1991],Goel and Qin [1994],Meyeret
al. [1995], and Gobton et al. [1997b]). Optimized vegetation
indices[e.g., l/erstraeteand Pinty, 1996; Gobton et al., 2000b],
on the other hand, attempt to overcomeor at least minimize
thesedrawbacksand havebeen appliedon a quasi-operational
basisto the detectionof vegetationon a globalscale[M•lin et
al., 2001]. The retrieval of one particularvariableof interest,
however,is often not sufficient,and more recent approaches
haverelied directlyon the inversionof physicallybasedmodels
to retrieve the state variables that control the radiation
transfer
processes
and hencealsothe observedsatellitemeasurements.
The many problems to be faced when addressingsuch an
inversionhave been extensivelydiscussedby Goel and Strebel
[1983],Pinty and l/erstraete[1991],Privetteet al. [1995], l/erstraete and Pinty [2000], and Kimes et al. [2000]. Onedimensionalphysicallybasedradiation transfermodels,which
typicallycomprisefrom five to sevenstatevariables,find their
usageprimarily for geophysicaland measurementconditions
where the usual homogeneousplane-parallelassumptionsare
acceptable[Gobton et al., 1997a]. Three-dimensionalmodels,
on the other hand, describe in more detail the structure of
vegetationcanopiesand thusmay require a muchlarger number of input parameters,for example,Kranigkand Gravenhorst
[1993], North [1996], Chen et al. [1997], and Govaertsand
1985;Belwardet al., 1990;Lovelandet al., 1991;Borel and Gerstl,
Verstraete[1998]. These explicit canopy architecturemodels,
1994;Hansenet al., 1996;DeFrieset al., 1997;Belward,1999].
however,have only recentlybeen employedfor designingreAlthough many important resultshave been obtainedwith
trieval methodologies[Govaertset al., 1997;Myneniet al., 1997;
these methodologies,they are often largely empirical or only
Knyazikhinet al., 1998]. As pointed out by Gobton et al.
regionallyapplicable;somerequire long-termstatistics,exten[2000a],the choiceof the model depends,firstly,on the nature
sive human interactionsor are prone to errors due to atmoof the application,but alsoon the accuracythat is requiredby
Copyright2001 by the American GeophysicalUnion.
the end user of the derived information, as well as the avail-
Paper number 2000JD000276.
ability and quality of the data, and the allowablecostof producingthe desiredinformation.
0148-0227/01/2000JD000276509.00.
33,405
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WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL CONIFEROUS FORESTS
Of interest here will be coniferous forests, where a realistic algorithmand the intrinsicuncertaintiesassociatedto the indescriptionof the canopystructureand an accuraterepresen- put remote sensingdata.
tation of the radiative transfer are essential to a better underBecauseof the nonuniqueness
of solutionsto an inversion
standingof the canopylight interceptionpropertiesand the problem, the interpretationof remotely senseddata strings
retrievalof other biophysicalcanopyparameters.In thispaper requiresfindingthe set of probablesolutionsrather than the
we will thusexplorethe potentialuseof a look-uptable(LUT) "true" solution.This strategyis,in fact,dictatedby (1) inherent
based approachto retrieve an exhaustiveset of vegetation limits of radiationtransferregimes,whichdo not guaranteethe
characteristics
from remotely senseddata. This is achievedby existenceof a uniqueone-to-onerelationshipbetweenthe sets
precomputingdirectionalreflectances
over explicitlymodeled of statevariablesof the geophysicalsystemand its measured
forestscenes,and for a large rangeof modelparametervalue outgoingradiancefields[Verstraete
et al., 1996],and (2) intrincombinations,before the actualinversionprocessis attempted sicuncertaintiesin both the inversionalgorithmand the data
[Gobronet al., 1997a;Govaertset al., 1997;Kahn et al., 1997]. sources.
Thisimpliesthat identifyingthe ensembleof the probThis then allowsnot only to solvethe inverseproblemwithin able solutionsmust be constrainedby the various levels of
operationalcomputingconstraintsbut also to estimatethe uncertaintiesassociatedto the various constituentsentering
accuracyof the retrievedsurfacetype information,by control- the inverseprocedure.A practical and elegant method has
ling the largest tolerable discrepancybetween the satellite- been devisedby Kahn et al. [1997] to extractaerosolload and
measured and the model-precomputedreflectancevalues. propertiesfrom dataacquiredby the MultiangleImagingSpecConsequently,the reliability of this retrievedinformationwill troradiometer(MISR) instrumenton board TERRA. It has
increaseasfurther(spectralor directional)instantaneous
mea- beenappliedbyPintyet al. [2000a]to estimatesurfacebidirecsurementsbecome availablefor a given observedtarget [Go- tional reflectance factor (BRF) and albedo values using
bron et al., 2000a]. More specifically,the proposedinversion METEOSAT data. This approachis basedon an ensembleof
methodologywill be applied over northernEurope, usingre- metricsthat permit the isolationof the set of probablesolumote sensingdata gatheredin the blue, red, and near-infrared tionsthat are all equivalentin termsof their abilityto reprebands of the VEGETATION instrument.Section2 presents
sent the observations
within the limits of the imposeduncerthe conceptualideas,the physicsand mathematics
supporting tainties.
the retrieval strategy.In section3 the applicationof the retrieval methodologyto a set of VEGETATION-P (top of at- 2.1. Forest Canopy Modeling
mospherebidirectional reflectancefactors) productsover
The inverseproblemto be solvedin the contextof thisstudy
Scandinaviais presented.
consistedin the identificationand primary characterizationof
2.
Strategy of Retrieval
The physicalinterpretationof a multispectraland/ormultidirectionaldata stringcollectedby a space-bornesensorover
terrestrial surfacesreducesultimately to the solution of an
inverseproblem. Inverse problemsare notoriouslyill-posed
and often lead to the identificationof multiplesolutions,hereinafter referred to as "probable"solutions,which are all statisticallyequivalentto the extentthat they permit the simulation of this series of observationswithin the range of the
remotesensingdatauncertainty[seeKahnet al., 1997;Gobron
et al., 1997a; Martonchik et al., 1998; Knyazikhinet al., 1998;
Pinty et al., 2000a; Gobronet al., 2000a]. Conceptually,these
probablesolutionsare simplypart of a verylarge ensembleof
"potential"solutionsthat mustbe regardedasinfinite,without
any a priori knowledgeon the type of solutionsto be found.
For all practicalpurposes,however,this ensemblebecomes
finite as soon as a LUT of potential solutionswith defined
boundaries
and increments
on the values of the variables
is
specified.The size of this LUT can be limited by importing
additional, so-called ancillary information to prevent the
searching
for veryimprobablegeophysical
or ecologicalevents.
Apart from the identificationof a large ensembleof potential solutions,the quantificationof the desiredaccuracyon the
retrievedinformationand the knowledgeof the remotesensing
data and algorithmuncertaintieshaveto be addressed.This is
mandatoryin order to bestselectboth the modelsto be applied
and the source of the data to be used in the inversion.
How-
ever, assessing
the accuracyrequired on the desiredinformation may not alwaysbe an easytaskunlessthe further impacts
related to the costof the toleratederrorsare thoroughlyevaluated. In practice,most end-usersrequestthe most accurate
informationthat canbe provided,giventhe performanceof the
borealforestsovernorthernEuropeanregions.As such,it was
necessaryto first identify the ensembleof potential surface
typesthat couldoccurwithin the regionof studyprior to the
simulationand subsequent
storageof their reflectancefieldsat
the top of the atmosphere(TOA) in an appropriateset of
LUTs.
Coniferous trees exhibit nonuniform foliage distributions,
highlyconvolutedneedlesurfaces,and significantclumpingof
their individualcrownelements(needles,shoots,and branches). They may well have in excessof 100,000needlesper tree
and can occur in stands with densities above 1000 trees/
hectare. Ecologicalfactorslike soil type and moisture,tree
densityand maturity,aswell as the exactgeographiclocation,
wind regime, and climatic conditionsalso play an important
role in the crown structure characteristics
of individual
trees.
The numberof possiblecombinations
for the parametervalues
available for the generation of realistic three-dimensional
(3-D) tree representations
thusincreasesdramaticallyif each
and everymeasurablestructuraldetail (e.g.,branchingangles,
needlegrowthlengths,and the sproutingbehaviorof different
budsand shoots)is taken into consideration.
Even more so if
severaltree speciesare to be consideredand the natureof the
modeledsceneis suchas to includesignificantallometricvariations.Consequently,the accuratethree-dimensional
description of conifers has either been limited to relatively small
spatialareas [Kranigkand Gravenhorst,
1993;Kranigket al.,
1994] or else, substitutedwith statisticaldescriptionsof the
variouscanopystructureelements[Li and Strahler,1985;Chen
andLeblanc,1997;GerardandNorth, 1997].Sincemanyof the
individualtree-structuralpropertieshave no obviousconnection to the lightinterceptionregime[Stenberg
et al., 1994a]and
thus shouldnot be modeled in isolation,some degreeof canopy abstractionbecomesnecessaryto avoid prohibitivere-
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL CONIFEROUS
FORESTS
33,407
quirementson computationalpowerand memory,especiallyif lander, 1988], even though the shoot orientationsfor Scots
large spatialareasare to be modeled.
pinesare dependenton the standage and canopydepth [StenThe approachadoptedhere attemptsat minimizingthe level berget al., 1994b].
The spatialdistributionof the coniferswas simulatedwith a
of structuralabstractionwhen representinga typicalnorthern
European
coniferous
forestat a spatialresolution
of 1 km2.At Poisson model regardlessof the stand densities [Wu and
the sametime, everyeffort was made to remain as faithful as Strahler,1994;Franklinet al., 1985].Five differenttree densipossibleto the documentedstatisticalbehavior of thosevari- tiesfrom 100 to 1200stem/hectarewere chosento generatethe
ablesthat are primarily responsiblefor the radiative transfer coniferousforest scenes.For tree densityvaluesgreater than
processes
within three-dimensional
vegetationcanopies.More 400 stem/hectare(whichis equivalentto 40,000treesin a scene
treesweregroupedintoheightclasses
of
specifically,
the forestmodelingprocessutilizesthe tree height, of 1 km2),individual
the crowndimensionsand spatialdistributionof the trees,the 0.5 m interval,and their biophysicalpropertieswere alignedto
size,amount,orientationand distributionof the foliage in the thoseof that respectiveclass.This explainswhy in Table 1 the
tree crownsas well as the various spectralpropertiesof the averagevaluesfor the tree height,the heightto the crownbase,
needles,trunks,and soil. A schematicdescriptionof the tree- and the crown and trunk radii are different at higher stem
modeling approach,which focusesentirely on surfacetypes densityvalues.Finally,the spectralpropertiesof the needlesof
composed
of a singledominanttree species,is givenin Plate 1. Norway sprucetrees [Williams,1991] were weightedwith the
For a givenstem densityand scenedimensionthe number of spectralbandsof the VEGETATION instrumentto yield an
treesto be distributedwascomputed.In accordancewith Haf- estimate of typical coniferousneedle reflectancesand transleyand Scheuner[1977]andLi and Strahler[1985]a lognormal mittancesin the blue, red, and near-infrared(NIR) domains.
[11C
c.... was •.,lt•J•tt
• .... such
tree height model was generated for a given average tree The lower boundary coi•muon of .......
heightandheightstandarddeviation.The allometricequations asto mirror the reflectancevaluesof a darkish,moistsoil (see
of Cermgket al. [1998]were usedto retrievethe corresponding Table 2 for all spectralproperties).
valuesfor the height to the crownbase,the crown and trunk
radius,andthe leaf area index(LAI). Althoughtheseallomet- 2.2. Physics of Retrieval
The identificationof a particularterrestrialenvironmentor
ric equationswere originallyderivedfrom a medium density
plot of Scotspine (PinusSylvestris)
in Belgium,comparisonof biome type on the basisof data taken from spaceinvolvesthe
the individualvariableswith data from BOREAS [Fournieret reckoningof the spectralBRFs at the surfacelevel. Once these
al., 1997; Shugartand Nielsen,1994] suggestedtheir appropri- surfaceBRFs are known, they can be comparedto a seriesof
atenessfor the modelingof a typicalborealconiferousforestat LUTs in order to identify the geophysicalsystemsbest able to
a scaleof 1 km2. In addition,the data from BOREAS were replicatethem. This implies,however,that the surfaceBRFs
collectedfrom standswith a broad range of tree density,ma- can be obtained from TOA BRF data and also that a model
turity, and speciescomposition(seePlate 2), thusbeingsome- exists to establish the link between the directional surface
what similar to the nonuniform stand conditions that are to be
reflectancesand the associatedgeophysicalsystems.The modexpectedwithin the scalesof interest here. The LAI from eling toolsrequiredto achievethis task thereforeinclude(1)
Cermgket al. [1998] varies between -2 and 6 for individual an atmosphereradiative transfer model allowing the simulations of TOA BRF valuesin the desiredspectralbandsof the
trees of 17.5 to 25 m height; this is comparableto the LAI
values reported by Chen et al. [1997] during the BOREAS sensorand with a prescribedset of atmosphericand surface
campaignand thosethat Stenberg
et al. [1994b]found for Scots properties,(2) a model approximatingthe surfacereflectance
fields in order to representthe lower-boundaryconditionfor
pine trees in Sweden.
Limitationsin availablecomputermemory (-0.5 Gb) re- the atmosphericmodel, and (3) a radiative transfer model
quireda simplifyingof the canopyarchitecturerepresentation, capableof representingthe radiation transfer regime and the
such as to allow for the simulation of realistic stem density top-of-canopy(TOC) BRF fields for various surface types.
ranges(10,000-120,000
trees/km
2) withinthe nominalspatial More specifically,the implementationof our retrieval scheme
resolution of the VEGETATION
instrument. From Plate 1 it
made use of (1) the SecondSimulationof the SatelliteSignal
can be seenthat to reducethe number of geometricalobjects in the Solar Spectrum(6S) model from Vetmoteet al. [1997],
in the canopysimulation,eachtree crownwasassembledusing whichpermitsthe simulationof TOA BRF fieldsundervarying
a singlecone and cylinder [Chen et al., 1997], both of equal surfaceand atmosphericconditions,(2) the parametricRPV
radius and height. This partitioningwas somewhatarbitrary modelfrom Rahmanet al. [1993],capableof representingTOC
but nevertheless
justifiablebecause(1) apex angle (the apex BRF fieldsover a largevarietyof terrestrialsurfaces,and (3)
angleis half of the solidangleof the conewith respectto the the Raytran modelfrom Govaertsand Verstraete[1998]impledownwardvertical)variationsare knownto be highlyvariable mentinga ray-tracingapproachto simulatethe radiationtrans[Li and Strahler,1985]andbecause(2) this structuralsubdivi- fer regime of complexthree-dimensionalsurfaceenvironments
sion allows for the approximationof the vertical leaf area at any spatialscale.
distribution,which in conifersis often skewedtoward the light
On the basisof the informationand hypothesesof section
source [Massman,1982; Chen et al., 1997]. The latter was 2.1 the canopytypescharacterizedin Table 1 were generated
accountedfor by placing75% of the total LAI for a giventree over a groundarea of 1 squarekilometer (seebottompanel in
into the volume of the cone and 25% into that of the cylinder. Plate 1 for a visualrepresentationof a 400 stem/hectareconifFor simplicity a uniform foliage distributionwas assumed erousforest). The Raytran model was subsequently
used to
within the tree crowns and individual
needles were simulated
with a surfacearea equivalentto that of a 5 cm long and 2 mm
wide cylinderwithout end caps.Additionally, an azimuthally
independent (uniform) needle orientation distribution was
adoptedthroughoutthe crownvolume [Oker-Blomand Smo-
simulate the transfer of radiation in all forest scenesand, in
particular,to generatethe TOC BRFs at discreteobservation
angles in the blue, red, and near-infrared bands of the
VEGETATION instrumentand at varioussolarzenith angles
that couldoccurover high latitudes.For everyforestscenethe
33,408
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL
Model Input Parameters:
CONIFEROUS
Tree
Model
AverageTree Height Im]
HeightStandardDeviation[m]
StandDensity [tree/hectare]
SceneSide Dimension |m]
FORESTS
Architecture:
Rad•,.•,
Log-normal Tree Height Distribution:
Allometnc
Equations
TreeHeight...
!tin,: Ira]
Spatial
Distribution
Plate 1. Schematicrepresentationof the canopy architecture model. The input parameters are used to
generate a lognormaltree height distribution.Every value of which is then related, via a set of allometric
equations,to the remaining architectural parameters needed to constructthe tree representationsin the
modeledscene.
Thetreesarethenrandomly
distributed
overa 1 km2 areato providetherequiredtreedensity
(shownis an obliqueview of a coniferousforest with a mean height of 15 m, a height standarddeviationof
1 m, and a standdensityof 400 trees/hectare).
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL
CONIFEROUS
FORESTS
33,409
Dosheallines: Cermok el ot (1998)
Solidline:Fournier
et oi (1997)
]•
..
Symbols:
TE22doto(BOREAS)
!
0
,"
•
o
Ill
Ill
•
/
0
5
10
15
20
25
DBH[cm] or H,, [m] or H,_ [m]
Plate 2. Comparisonof allometric equationsfor Scotspine in a medium densitystandin Belgium [CermSk
et al. 1998]with the correspondingdata from the TerrestrialEcology(TE)-22 group of the Boreal EcosystemAtmosphereStudy(BOREAS)' heightto crownbaseversustree height(green) and crownradiusversustree
height (blue). The divergencebe•een the relationship for the tree height and diameter at breast height
(DBH) for Scotspine and that of Fournieret al. [1997] for Old Jack pine (red line) could be related to the
low-correlation
coefficient
(R 2 • 0.52) of the latteranditsreducedamountof datapointsabove18 m.
generatedTOC BRF fields were approximatedby the RPV
model [Rahmaneta!., 1993],which on the sole basisof three
parameters permits to interpolate/extrapolate between the
BRF data points simulated at discreteobservationanglesby
the Raytran model. The RPV model also providesa very efficient mathematicalway to expressthe BRF fields which can
then be entered into the 6S model [Vetmoteet al., 1997] to
generatean ensembleof potential solutionsat the TOA. The
latter were used to interpret the VEGETATION data acquired under any angularconditionsand for a range of atmosphericproperties.A novel techniqueto estimate the set of
optimal RPV parametersand their associatedrange of uncertainty wasdeveloped[Gobtonand Lajas, 2001] and appliedto
each and every forest scene.Table 3 summarizesthe various
geophysical
scenariosadoptedto simulatethe TOA reflectance
fields.
As an example of the information stored in the LUTs,
Plate
3 shows the simulated
TOA
reflectance
2.3.
2.3.1.
Identification of the ensemble of probable solutions.
To apply the simulatedspectralsignaturesin a boreal forest
identificationalgorithm,an efficient measuremust be devised
Tree
stem/ha
100
200
400
801
1209
LAIs.....
Fractional
m2/m2
Covera
0.15
0.30
0.63
1.26
1.83
0.04
0.08
0.16
0.33
0.48
for the
Mathematical Approach
Table 1. Actual Canopy BidphysicalCharacteristics
Density,
factors
blue, red, and NIR VEGETATION channels(top three rows),
for a solar zenith angle of 49ø and a relative azimuth of 45ø
over low, medium, and high tree density boreal coniferforest scenarios(left, middle, and right columns,respectively).
The large dynamicalrangesoccurringat all wavelengthsof the
VEGETATION sensorillustrate both the importance of the
angulareffectson the measuredsignaland, more interestingly,
the existingpotential in usingtheseangularsignaturesto better
constrainthe inversionprocedure.
•Ic.... u....
Radc.....
m-
m
cm
m
1.14
1.14
1.14
1.12
1.12
6.73
6.73
6.73
6.54
6.54
15.00
15.00
15.01
14.76
14.76
10.43
10.43
10.42
9.93
9.93
RadTmnk,
•-IT....
aRatio of the verticallydownwardprojectedarea of all trees in the sceneto the area of the scene.
33,410
WIDLOWSKI
ET AL.: CHARACTERIZATION
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CONIFEROUS
FORESTS
1200 stem•nectare
o
O.
o 2.5
20
0
0
0
.r -
o.o5
o.15
0.25
0.35
'r .,, 0.05
0.45
0
-60
-•
20
••
•
0
!
.
-i
0.15
0.25
0.35
!
I
I
0.45
20
•,
e, [•1
O55
0.50
0.25
020
o•1o
ø
-'r -
0.05
0 15
0.25
0;55
0.05
0 45
_,,
-
-20
0
20
40
,•,1
-40
•0
O. 15
ii , ,_:_, i •,
-20
0 2.5
0-•5
, ! , , , ß .,
0
20
0.45
. i . . . i ,.
40
60
1
0•t''.;.•,,.,-..,-.,,;.,•;•:.
,.
/
.
• 0•0[
0.15
ø,,[
o
0 10
,r,•005O.
150.25
0.55
0.45
0•[•
.0.0•
015
0.25
0.•5
0.450.05[
0001.,
I
stem/hectare
•50350.45
I , . . , ß - - I . . . , . . . , . . . •
-co
-40
-20
0
20
400 stem/hectare
40
-40
-20
0
20
40
1200 stem/h•e
Plate 3. Example of precomputedspectralBRF valuesat the top of the atmospherebetween- 70 ø _< Ov-<
70ø, for a varietyof atmospheric
opticaldepths? at 550nm,at illumination
conditions
of 0o= 49øand
-
45ø.Every columnrefersto a differentsurfacetype (dark soil)whichis visualizedfrom the localzenith in the
bottom panel. The first three rows(top to bottom) refer to the blue, red, and near-infraredchannelsof the
VEGETATION
instrument.
WIDLOWSKI
ET AL.' CHARACTERIZATION
Table 2. SpectralCharacteristics
fit
459 nm
662 nm
834 nm
OF BOREAL
CONIFEROUS
FORESTS
33,411
value of the error criterion e belong to the set of probable
DNeedle
TNeedle
lOTrunk
0.0097
0.0505
0.4900
0.0153
0.0532
0.4620
0.0800
0.1425
0.2350
2 or)< 1 I e} at that particularviewzenith
S { X(x,
DSoil solutions
angle and spectralband condition.However, for a potential
0.0670
solution to be identified as a probable solution to all valid
0.1270
instantaneousdirectionaland spectralobservations(O) of a
0.1590
given instrument,it needsto be includedin the intersectionof
all of the above availablesetsof probablesolutions:
weighted
wavelength;
p, reflectance;
,, transmittance.
S{probable
solutions
Io,
in order to extractthe probablesolutionsfrom the ensembleof
potential solutions.This can be achievedby evaluatingall potential solutionswith the followingspectralmetrics [Kahn et
al., 1997;Martonchiket al., 1998;Pinty et al., 2000b]:
In the case of a monodirectional
(2)
satellite,
such as
VEGETATION,
a probable
solution
isthusonewhose
valuesover the blue, red, and NIR bands(for claritythe subscript0vwill be omittedwheneverreferring to the monodirectionalVEGETATION satellite)are simultaneously
lessthan 1.
This permits identifying,for each overpassand pixel of the
Wcost(
,•, Or)[loSAT(
x, /-•,/-•0,4) -- 4)0)-- ioLUT(
'•, /-•,/-•0,4) -- 4)0)]2,
VEGETATION sensor,a set of radiatively consistentatmo[ O'total(/•,
0v)]2
, spheric and surface conditionsthat are accurate enough to
(1) interpret the VEGETATION spectral data strings,with an
where • and •o are the cosineof the observationand illumi- accuracyat leastequalto the error criterionor, equivalently,to
nation zenith angles,respectively,and 4) - &o denotes the the correspondingO'tota
1 (/•) in the denominatorof (1). This
relative azimuth angle between the directionsof observation procedure thus yields at once the values of the model state
and illumination.
variablesdocumenting(1) the aerosolload provided as an
In (1), iO
SAT(,k,]&,]&0,qb-- qb0)
is the spectral
TOA BRF effectiveaerosolopticaldepth at 550 nm and (2) the stateof
value measuredby the space-borneinstrumentat the current the coniferousforestsas describedin the geophysicalscenes
band,kandviewzenithangle0•,,pL.t•T
(X, •, •o, 4)-- &o)isthe entering the Raytran model. The latter correspondsto the
model-generatedspectralTOA BRF value at the currentband surfaceboundary condition which is also mathematicallyex,k and view zenith angle 0,stored in the LUTs of the potential pressedby the RPV model with predeterminedsetsof RPV
solutions.Wco•,
t (X, 0•,) is a spectraland directionalweighting parameter values,namely, the spectralamplitude and shn.p•
factor, which can be chosen to account for either desired or
of the surfacescatteringfunction(po(A), k(A), OHC•(X)).
2.3.2. Selection of the most probable solution. For a
undesiredeffects,or elseset equal to 1 aswas done here. The
O'total
(h, 0•,) value accountsfor the expecteduncertaintyin numberof practicalreasonsit is appropriateto selectonly one
both the measured and the model-generatedspectral TOA
candidatefrom amongthe set of probable solutionsidentified
BRF valuesfor a given spectralband and view zenith angle. in (2) whichsufficientlyaccountfor the satellite-gatheredBRF
This latter stringof valuesis difficult to assesspreciselyfor a valuesat a particularpixel location.To find the most likely of
given instrumentusing a theoretical approach,since it accu- these probable solutions,we follow Pinty et al. [2000a] and
mulates all kinds of limitations of the instrument, as well as computethe meanvalue,p0(,•N•R),and averagedeviation,Apo,
uncertaintieson calibration coefficients,the stability of the of the associatedspectralamplitudesof the RPV model in the
instrument and geometricalrectification,and the inaccuracies near infrared. The most likely candidateto representthe uninherentto the modelingof the spectralTOA BRF values.The derlyingsurfacehO(XN•) is the one associatedwith spectral
choiceof the O'tota
I (/•, 0v) valuesimpactsthe numberof com- amplitudep0(,&N•R),
that is, firstly, amongthoseprobablesobinationsof surfaceand atmospheric
variableswhichrepresent lutionsthat are not further awayfrom pO(XNX•)than Apoand,
acceptablesolutionsto the inverse problem for every pro- secondly,
thatminimizes
thequantityP0(XNm)-- PO(XN•)I.If
cessedpixel: the larger its value the greater the number of due to different associatedatmosphericoptical depthsseveral
solutionsthat maybe consideredacceptablefrom the radiative probablesolutionswith •o(Xsm) exist,then the one with the
point of view. However, rather than usingthe uncertaintyval- smallest value of (X(x•x•)),
computed
astheaverage
overalln
ues of the BRF, the maximum tolerable deviation between
valid instantaneous bidirectional measurements in the NIR,
observedand modeledspectralBRF valueswill be expressed will be selectedfrom amongthem to representthe mostlikely
instrument
this
throughoutthis paper in terms of the error criterion, e, which solution. In the case of the VEGETATION
is set to a percentageof the satellite-measured
spectralBRF meansfinding the probablesolutionthat is associatedto the
ho(Xsm)withthe smallest
valueof X(XNm)
2
(seeFigure1 for a
All potentialsolutions
thathaveX(x,0v)
< 1 for a specific graphicalillustration).While this selectionprocedureis aimed
values.
Table 3. GeophysicalScenariosUsed to Constructthe Look-Up Tables
Medium/Model
Variable
Range of Values
Atmosphere/6S
model
[Vetmoteet al., 1997]
aerosolopticaldepth at 550 nm
Forest/Raytranmodel
[Govaertsand Verstraete,
1997]
tree density(stem/hectare)
mean conifer height, m
meancrownLAI, m2/m2
for a subarctic summer
0.05-0.45 in stepsof 0.1
modeP
•Watervaporandozonecontentof 1.0g/cm2 and320 DU are used.
100, 200, 400, 800, and 1200
15
3.81
33,412
WIDLOWSKI
ET AL.: CHARACTERIZATION
P0()•
Nm)
varyingaerosolloadsfor one
OF BOREAL CONIFEROUS
FORESTS
underlyingsurface,has to rely on the analysisof retrievals
obtainedduring a shorttime period if no additionalinformation or ecologicalmodelsare availableto compensatefor the
lack of appropriatesimultaneous
data. This assumes
however,
that the surfaceconditionsduringthe data accumulationperiod (typicallya few weeks) are not changingsignificantly
enough to impact the retrieved sets of probable solutions.
Furthermore,it is surmizedthat the numberof probablesolutions on any particularday is merelycontrolledby the occurrenceof a changingatmosphere,whichforcesthe surfacetype
to be chosensothat the radiativecouplingproducesacceptable
fits to the data measuredby the instrument.The selectionof
the mostrepresentativesurfacetype can thusbe achievedon
the identificationof likely frequent surfacetype belongingto
the ensembleof likely solutionsfound over the given time
period. Mathematically,the selectionof the mostrepresentative foresttypeis doneby estimatingfirstthe temporalaverage
of the mostlikely •O(/•NiR)over a fixednumberof days,N:
/•re-defined
surface
type
+Ap0
+
+
+
xx
P0()•
Nm)
o
o
o
AP
o
pre-definedsurfacetypes
with
different
#
P0
2
1
•- Z(•,NiR)
I
Spectralselectionmetrics
T
•0(•NIR)
: 7 E b0(ANIR)(t),
(3)
t=l
Figure 1. Graphicillustrationof the implementedprocedure
to retrievethe mostprobablecandidatefrom the ensembleof
probablesolutions(i.e., for symbolsthat lie within the grey where T is the number of availablevalues during the N-day
andbO(XNiR)
is the temporal
shadedareas).The x symbolcorresponds
to the surfacetype periodof temporalaccumulation,
that minimizesthe quantity[QO(,kNiR)
-- QO(,kNiR)[.
The white averagedvalueestimatedfor parameterbO(XNiR)(t
).
circular area identifieswhat associatedatmosphericaerosol
The N-day representativevalue for the Poparameteris the
loadyields
thelowest
•xNi•)value
forthisparticular
surfacevalueof bO(•.NiR)(t)that minimizesthe quantity[bO(•.NiR)(t)
type, i.e., the selectedsolution.
- •O(XNiR)[.
Sincethis solutioncorresponds
to one of the
at handlinglarge setsof probablesolutions,and to reducethe
influenceof atmosphericand data-relatedperturbations,it is
individualsolutionsselectedin the completeN-day time series,
the associateddiscretevaluesfor the k and OHO parameters
are easilyassessed.
This proceduredefinesthe mostrepresentativeN-day valuesof the three surfaceparameterscharacterizing the surface radiative properties, namely, bO(XNiR),
clear that various other means of extraction could be devised,
all of which with their own degree of arbitrariness.
2.3.3. Selection of the temporally most representative so-
lution. The output of the procedure in sections2.3.1 and
2.3.2canbe mappedin orderto locatethosepixelsthat belong
to at leastone of the broadsurface-type
categories
predefined
in the LUTs. However, in the presenceof cloudsand other
unfavorableatmosphericconditions,the situationmay arise
where none of the acceptablevaluesof e allowsfor a given
pixel to be associated
with one of the predefinedsurface-type
solutions.This is especiallythe caseif multiplesetsof spectral
BRF values,gatheredat different viewingconditions,have to
be in agreement. Conversely,monodirectional instruments
might very well allow for the retrieval of a mostlikely solution
in that case, but only at the risk of identifying a different
surfacetype than would be retrievedunder clear-skyconditions. Thus in the absenceof relevant simultaneousangular
samplingof the spectralTOA BRF values,the effectsdue to
the couplingbetweenthe surfaceand the atmosphericscattering propertiesare not alwaysseparablein a reliable manner.
Indeed, each individual set of spectralVEGETATION measurementsmaycorrespondto differentgeophysical
systems,
all
of themequiprobable
in the senseof (1) to explainthe observations. The selection of the most likely solution from the
ensembleof probablesolutions(in Figure 1) may therefore
very well be impairedby the particularillumination,viewing
and atmosphericconditionsat the time of observation.
A supplementalstep in the data interpretationis thus requiredin
order to providea coherentcharacterizationof the underlying
forest type. More specifically,the identificationof the most
representative
solution,whichbestsummarizes
the stateof the
•(/•NIR),andO/-/G(/•NIR)'
It alsoensures
thattheseselected
valuesare able to produceradiation fields consistentwith at
least one of the radiation fields, measuredduring the N-day
period by the instrument.
2.4.
Simulation
Tests of the Inverse
Procedure
A seriesof experimentswere performedin order to (1)
quantifythe intravariabilityof the set of predefinedpotential
solutions(the term solutionrefershere to an underlyingsurface type and its associatedatmosphericoptical depth), (2)
identify, in the case of monodirectionalinstruments,one or
more optimalviewingconditionsthat consistently
increasethe
separabilityof the predefinedsolutionsoverall spectralbands,
and (3) test the expectedperformanceof the proposedinversion methodologyin terms of the limits and quality of the
retrieved forest types and associated aerosol load for a
VEGETATION-like instrument.These tests[seePintyet al.,
2000c]lead to the conclusion
that (1) the intravariabilityof the
predefinedsolutionsis dependenton the spectralband of
observation,(2) differentoptimalviewingconditionsexistfor
individualsurfacetypes,illuminationangles,and atmospheric
aerosolconditions,and (3) no singlevalue of the error criterion, greater than the absolute calibration error of the
VEGETATION instrument,can guaranteethe completeseparability of all predefinedsolutionsin Table 3 under all illumination and viewing conditionstested. The latter refer to
solar zenith anglesof 40ø, 50ø, and 60ø, satellitezenith angles
goingfrom 0øto 80øin stepsof 1ø,and relativeazimuthangles
spanning0ø to 180ø in stepsof 5ø.
WIDLOWSKI
ET AL.: CHARACTERIZATION
Multiangular observationssuchas those of the Multiangle
Imaging Spectroradiometer
[Diner et al., 1991], on the other
hand, would almost completelyeliminate the viewing angle
dependencyof the separabilityof the predefinedsolutions.In
addition,the increasednumberof spectraland directionalconstraintsin (2) would almost certainly increasethe level of
separabilitybetweenthe predefinedsolutions.However, the
use of only one value for the error criterion in operational
retrievalsis still inappropriatebecauseof the dependencyof
the solutionswith respectto the changesin illuminationangle
and aerosolconditions.The approachadoptedherewill therefore consistof graduallyincrementingthe value of the error
criterion
until
either
a valid
solution
is retrieved
or else an
upperlimit of e is reached.In the caseof multiplesolutionsto
an instantaneously
gathereddata string,the methodologyof
section2.3.2 will be appliedto retrievethe most likely of the
probablesolutions,whereasthe procedureof section2.3.3will
be used to identify the temporallymost representativeof the
predefinedsurfacetypesat everypixel position.
OF BOREAL CONIFEROUS
FORESTS
87.70
8O
o
60
0
>• 4o
2o
10.z•5
1.46
3.
33,413
Application to Actual Data
1
2
3
0.33
o.o6
4
5
Retrieved number of probable solutions
The identificationand characterizationprocedurepresented
in section2.3 hasbeen appliedto a VEGETATION TOA data Figure 2. Histogram of the retrieved number of probable
set (P-products),acquiredover northern Europe during the solutionsoverScandinaviaduringthe first20 daysof June1999
first 20 daysof June 1999.Given that Henryand Meygret[2000] for all valuesof the error criteriabetween3 and 20% together.
claim that the absolute
calibration
error
over the first three
spectralbandsof the VEGETATION instrumentlies in the
range of 3 to 5%, the inversionprocedureat everypixel position was started with an initial error criterion value of 3%. If no
probable solution out of the precomputedset of potential
forest types could be retrieved, the error criterion was increasedby 1%, and equation(1) was evaluatedanew,for all
three spectralbandsconsidered.This iterative procedureeither identifiedone or more probablesolutionsat somespecified degreeof confidence(error level) or endedwith no solution if the error criterion reached 20% and no probable
solution had been identified yet. From results summarizedin
Figure2, it canbe seen,however,that the numberof probable
solutionsdid not exceedunity in 87.7% of the processedpixels
for which acceptablesolutionswere identified.
This procedurecan be appliedin a stand-alonemode in the
sensethat it doesnot require anyprescreeningfor cloud/cloud
shadowcontaminationor other spuriousgeophysicalsituations
becausethese geophysicalsituationscan hardly lead to the
findingof a probablesolution,as definedby (1). However, to
limit the unnecessaryprocessingof too many pixels, we
adopteda conservativescreeningprocedurethat is basedon
the optimizedVEGETATION normalizedindex(OVNI) [Gobtonet al., 2000b].Thisvegetationindexprovidesthe abilityto
assessthe presenceof vegetationon the basisof TOA BRF
valuessampledby the blue, red, and near-infraredbandsof the
VEGETATION instrument.We thusfirst labeled all pixelsof
the processedVEGETATION data set before applyingour
inversionprocedureonly on those pixels correspondingto a
vegetationclass.
sent a probable solution. (For the latitude and longitude
ranges of interest here (4øE-30øE and 55øN-71.25øN) the
VEGETATION 1 km "Plate carrde" projection had an average pixel size of -994 m in the latitudinal direction,while
that of the longitudinaldirectionvaried from -486 m at 55øN
to -600 m at 71.25øN,suchthat it had to be reprojected,using
a nearestneighborsamplingscheme,to obtaina true 1 km2
pixel size map.) White areas indicate the presenceof water
bodies or pixels for which no probable solution could be retrieved for any allowable value of the error criterion. From
panel to panel the maximum tolerable BRF deviation was
increasedwith the consequencethat more pixelssatisfiedthe
imposedconditionsand thus becamepart of the ensembleof
probablesolutions(greycolor).Clearly,the likelihoodthat one
of the predefined surface types is truly responsiblefor the
observed
measurements
decreases
as the constraints
on the
error criterion are being relaxed. Depending on the application, a compromisehas to be found between highly accurate
yet mostlyempty maps,on the one hand, and relativelyinaccurate but well-coveredmaps on the other hand. In any case,
the reliability of the adopted retrieval methodologycan be
assessed
over sparselyforestedregionslike Denmark (4-12%
forest cover [Piiivinenand K6hl, 1996]), where only very few
forest-coveredpixel locationswere identifiedin Figure 3, even
for e = 20%.
The right-handpanel of Plate 4 showsthe atmosphericoptical depth field associatedwith the retrievedforest-typesolutionsin Figure 3, while the left-handpanel displaysthe spatial
distributionof the error criterion valuesyielding at least one
3.1.
Forest Identification
solution.The associatedoptical depth field is actually a temFigure3 shows,for a seriesof valuesof the error criteria,the poral mosaicof opticaldepthvalues,eachone associatedwith
spatialdistributionof thosepixels,whosemost representative a particular day of the compositingperiod but not necessarily
spectralBRF valuesat TOA (overthe periodfrom 1 to 20 June being statisticallyrepresentativeof that period.
As the error criterion value is increased and more and more
1999) were sufficientlycloseto thosein the LUTs, suchthat
at least one of the predefined surface types would repre- pixelsare associatedto the predefinedsurfacetypes,the prob-
33,414
WIDLOWSKI ET AL.: CHARACTERIZATION
Error Criterion = 5 %
Error Criterion = 15 %
OF BOREAL CONIFEROUS FORESTS
Error Criterion = 10 %
E•or Criterion = 20 %
Figure 3. Forestidentificationmap for the first 20 daysof June 1999with a maximumtolerableBRF
deviation
at various
accuracy
levelsbetween
5 and20%.Vegetation
coverage
(greyareas)increases
asthe
errorcriterion
increases.
Whiteareas
indicate
thepresence
ofwaterbodies
orlocations
forwhichnoprobable
solutioncouldbe retrievedat that particularvalueof the error criterion.
abilitythatthe actualbiophysical
characteristics
of thesepixel
locationsare in agreementwith the onesof the predefined
solutions
will decrease.
The goal is thusto generatea forest
covermapwherethe correspondence
betweenpixelswithvalid
retrievalsand the actualpre.senceof forestedareasat these
locationsis maximized.
This canbe achieved
by selecting
a
cutoff value for the maximum tolerable error criterion that
the abovearguments,
it wasdecidedto presentthefollowing
results at a maximum value of 8% for the error criterion. This
valuecorresponds
to the in-flight,root-mean-square
deviation
from the VEGETATION referencecalibrationover bright
sandsand deserts in the blue, red, and near-infrared bands
[Henryand Meygret,
2000].Additionally,
the 8% specifically
takeaccount
of thebounded
envelope
of thepredefined
spectral reflectances
in the LUTs. In otherwords,it captures
the
adequatelydelineatesthe presenceof woodedareassimilarto
thoseincludedin the setof predefinedforesttypes.Clearly, inaptitudeof the five predefinedforestscenesto accountfor
sucha valueis dependenton the adequacy,
the number,and the naturalrangesof structuralandspectralvariabilityin the
rangeof surfacetypesthat are includedin the setof potential landcoverand soil typesthat are likely to be encountered
solutions.
More limiting,however,becauselessquantifiable within the regionof study.
with respectto the inversionprocedure,are processing
and
measurement-related
uncertainties,
like datainterpolationfor 3.2. Forest Characterization
mappingpurposes,adjacencyeffects,and spectralmixing,
Beyond the task of forest identification,the inversionmethwhichall affectthe denominator
of (1). As a compromise
to odologyalso aimsat characterizing
the underlyingwooded
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL CONIFEROUS
•.•
..
•
33,415
AssociatedOpticalDepth Map
Error CriterionMap
.
FORESTS
. ..
•..•
.
,•. •q .i•.••.%-.•.•,:'
•. v••..: .-'•
.' '
. '-.•
•'
"•'
•.•
' -.:to:.•,• , t-
- -
• - ß.• . .
.....•'•
•. ß•,.,•
AssociatedOpticalDepth @ 550 nm
Error Criterion [%]
3
4
5
6
7
8 9 10 11 12 13 14 15 16 17 18 19 20 No Fit
No Fit 0.05 0.15 0.25 0.35 0.45
Plate 4. Accuracymap for the first 20 daysof June 1999with the maximumtolerablespectralBRF deviation
varyingbetween3 and 20% of the observedvalues(left). White patchesindicatethe presenceof water bodies
or bright surfaces.Optical depth map for the first 20 days of June 1999 (right). These values are those
associatedto the most representativesurfacetype (and day) of the compositingperiod.
surfacetypes.The le•hand panelof Plate5 showsa forestcharacterizationmap of Scandinavia,
derivedfrom VEGETATION
data for the first 20 daysof June 1999,with the O'tota
I (h) value
set to 8% of the spectralBRF values.A general thinningof
tree densitywith latitude canbe observedin Finland. The same
effect is alsoapparentover Swedenwith the additionof orography-inducedtree densityreductionsin its western regions.
The right-handpanel of Plate 5 exhibitsa map derived in the
contextof the Pan-EuropeanLand Use and Land-CoverMonitoring (PELCOM) project [Muecheret al., 1998]. This map
was generatedby integratingmultiple sourcesof land use informationwith information resultingfrom the classificationof
multitemporalNOAA-AVHRR satellitedata and thuscannot
be easilyassociatedto one specifictime period or year.A visual
inspectionof the two reveals a high degree of correlation,
especiallyin the delineationof the contoursof the broadvegetationclasses
(e.g.,the presenceof the "arableland" class,for
example, is well respectedby our forest identification algorithm). It shouldbe stressedthat the VEGETATION-derived
map was generatedfrom 3 weeks of data only and does not
require any further ancillaryinformation.The summerperiod
wasprimarilyselectedin order to ensurea sufficientnumberof
cloud-freeconditionsover northern Europe and therefore to
providethe best possiblespatialcoverageover this region.
Plate 6 comparestwo regionsof Plate 5 with the corresponding areasof a land-covermap derived in the contextof the
ForestMonitoringin Europe with Remote Sensing(FMERS)
project.The FMERS-I mapswere derivedfrom the analysisof
1 year of data gathered in 1997 from the high-resolutionIRS
1C WIFS sensorand ancillaryground-controlledobservations
[see Hame et al., 1999]. Again, a high degree of agreement
between the forested structuresin both regionsis evident. In
the top panels(southernSweden)our methodologyappearsto
account
for the transition
between
coniferous
and deciduous
forests along the southwesterncoastal region. The possibly
spuriouslocation of forestedpixels along some of the coastlinesis presumablydue to the way the originalVEGETATION
data are interpolatedwhen projectedonto the user-specified
grids.
Figure 4 indicatesthe relationshipbetween the measured
and the modeled BRFs at the top of the atmosphere.Indeed,
it canbe seenthat the selectedprobablesolutionsall lie within
the uncertaintylevel of (1), expressed
by an error criterionof
8%.
When comparingthe maps in Plates 5 and 6, one should,
however,keep in mind the variousuncertaintiesdue to data
derivedfrom differentsources,with disparatereliabilities,spatial accuraciesand temporal acquisitionperiods, as well as
those due to the setsof criteria that are not alwaysrelated to
measurablequantitiesbut instead to ecologicalclassesthat
havebeen defineda priori. Two of the main issuesin suchan
intercomparisonexerciseare the selectionof a tolerable error
level and the lack of quantificationof the uncertaintiesin the
associatedclasses.By precomputingthe radiativesignatureof
a seriesof explicitlydescribed,three-dimensionalrepresentationsof potentialland-covertypes,prior to the identificationof
33,416
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL CONIFEROUS
FORESTS
Plate 5. Forest characterizationmap for the first 20 daysof June 1999 with a maximum tolerable BRF
deviationof 8% (left). White patchesindicatethe presenceof water bodiesor bright surfaces.Land-cover
characterizationmap derived in the context of the Pan-EuropeanLand Use and Land-Cover Monitoring
(PELCOM) project (right).
the mostprobableone of theseas a representativesolutionfor
a givenperiod of observation,the ambiguitiesof interpretation
are ultimatively delegated to the end user of the derived information. It is his task to decideupon the value of the error
criterionbeyondwhich the predefinedsolutionscan no longer
be tolerated as probable in the senseof the application at
hand. In the caseof the presentedinversionmethodologythe
retrieved information will not only contain biophysicalstructure parametersbut alsothe allometricequationsthat relate to
them, since these were an implicit part of the input information.
Nevertheless, a number of issuesrelated to the derivation of
the forest characterizationmap in the left-handpanel of Plate
5 still haveto be addressedhere. The lengthof the compositing
period and the number of days with at least one probable
solutionwithin that period both have an effect onto the selection of the most representativesurfacetype. The reasonsfor
this are twofold at least:firstly,the VEGETATION instrument
is monodirectional,which clearly reducesits capabilityin differentiatingbetweensimilarlyvegetatedsurfacesat the scaleof
observation[Dineret al., 1999],and secondly,it is alsopossible
that the viewing conditionsfor a given pixel location may
changefrom relativelyoblique anglesto virtually nadir in subsequentoverpasses,
which can easilyinfluencethe separability
of the predefinedsurface-typesolutions[Pintyet al., 2000c].
Additionally, one has to keep in mind that with the rapidly
changingatmosphericconditionsover northern latitudes,the
monodirectionalityof this instrument,and the associatedmeasurementerrors, it is possibleto mistakenlyretrieve a unique
solutionto the inversionproblemthat maybe characterizedby
an elevatedaerosolload and an "erroneous"surfacetype.
4.
Conclusions
The retrieval
of an exhaustive
set of boreal forest character-
isticsfrom remote sensingdata was investigated.For that purposethe BRF fields at TOC and, subsequently,at TOA, for a
seriesof three-dimensionalrepresentationsof typicalconiferous forests,were simulated,for a predefinedset of aerosol,
viewing,and illumination conditions.These spectralBRF values were stored in LUTs, from where they could easily be
retrieved for a comparativeevaluationagainstactual satellite
data, under identical viewing and illumination geometries.If
an acceptablematchbetweenobservedand precomputedspectral BRF valueswas found, the physicalparametersthat had
been involved in the generation of the correspondingLUT
entrieswere consideredas a possiblesolutionto the satisfactory interpretationof the observedsatellitemeasurements.By
retaining the most likely of the so retrieved solutionsover a
periodof 20 daysin June 1999,the generationof mapsshowing
the likelihood of identifyingvarious forest types, their most
representativecorrespondingstructuralcharacteristics,
aswell
as the associatedatmosphericoptical depth at the day of retrievalwasperformed.A visualcomparisonwith two independent land-cover maps confirmed the usefulnessof this approach, especiallywith respect to the delineation of forestcovered zones. In addition, however, the proposed retrieval
methodologyis completelygenericand thuscan easilybe pro-
WIDLOWSKI
ET AL.:
CHARACTERIZATION
OF BOREAL
CONIFEROUS
FORESTS
33 417
-8%
FMERS-I project
-
.
.
Coniferous
Broadleaved
Tree Density(stem/hectare)
Forest
D•iduous
1200-
Forest
800
BroadleavedEvergreenForest
Mixed
Coniferous / Broadleaved
Forest
400
Wooded Land-
Coniferous
200
Wooded Land-
Broadleaved
100
Other Land
Southern
Swi•den
Esti•nia
& IJatvia
No Fit
Plate 6. Comparisonbetweenour forestcharacteristics
map for June 1-20, 1999(with an error criterionof
8%), and the land-covermap producedin the contextof the FMERS-I projectover (top) southernSweden
and (bottom) Estoniaand Latvia.
totyped for different instruments,suchas MODIS, MERIS,
SeaWiFS, etc.
Of particularinteresthere is the latestof the generationof
multispectraland multiangularinstruments(e.g., MISR). Indeed,Diner et al. [1999]and Gobtonet al. [2000a]showedthat
with multiangularMISR data, a more detailed,reliable, and
accuratedescriptionof different surfacetypes is achievable
than from monodirectionalinstruments.More specifically,its
nine viewingangleswould allow for a much more stringent
identificationcriteria to be imposed,when lookingfor probable solutionsin the LUT entries. This, in turn, would clearly
translate into a better separabilityof the set of predefined
solutionsand hencealsothe acceptablesizeof that ensemble.
Another point to consideris that at the nominalgroundresolution of MISR (275 m) the use of three-dimensional
canopy
representations
for the simulationof the exitingBRF field at
TOC intuitively appearsto be more warranted.All of these
considerations
then indicatethat the potentialof the method-
33,418
WIDLOWSKI
ET AL.: CHARACTERIZATION
OF BOREAL CONIFEROUS
FORESTS
Leaf area index of boreal forests:Theory, techniquesand measurements,J. Geophys.Res.,102, 29,429-29,443, 1997.
DeFries,R., M. Hansen,M. Steininger,R. Dubayah,R. Sohlberg,and
J. Townshend,Subpixelforest coverin Central Africa from multisensor,multitemporal data, Remote Sens.Environ., 60, 228-246,
0.30
// //1//•/•
3x8770
datapoints
0.25
1997.
/
/ /
//'
//-870
0.20
_
0.15
Diner, D. J., et al., A Multi-Angle Imaging SpectroRadiometerfor
terrestrialremote sensingfrom the Earth ObservingSystem,Int. J.
ImagingSys.Technol.,3, 92-107, 1991.
Diner, D. J., G. P. Asner,R. Davies,Y. Knyazikhin,J.-P.Muller, A. W.
Nolin, B. Pinty, C. B. Schaaf,and J. Stroeve,New directionsin earth
observing:
Scientificapplicationsof multiangleremotesensing,
Bull.
Am. Meteorol. Soc., 80, 2209-2228, 1999.
Fournier, R. A., P.M. Rich, and R. Landry, Hierarchicalcharacterizationof canopyarchitecturefor borealforest,J. Geophys.
Res.,102,
29,445-29,454, 1997.
Franklin, J., J. Michaelsen,and A. H. Strahler, Spatial analysisof
density pattern in coniferousforest stands, Vegetatio,64, 29-36,
1985.
0.10
/ '-+"
0 NIR
+
•
0.05
l ....
0.05
i
....
I
0.10
....
i
0.15
Measured
....
0.20
BRF
at
I
0.25
RED
BLUE
....
0.30
TOA
Figure 4. Relationship between modeled and measured
BRF values at the top of the atmospherefor the first three
bands of the VEGETATION
sensor for a section of the forest
characterizationmap in the left panel of Plate 5 (error criterion = 8%).
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in the solar domain, in Proceedings
of the 7th ISPRS International
Symposium
on PhysicalMeasurements
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in RemoteSensing, pp. 267-273, A. A. Balkema, Brookfield, Vt., 1997a.
Gobron, N., B. Pinty, and M. M. Verstraete,Theoreticallimits to the
estimationof the leaf area index on the basis of optical remote
sensingdata, IEEE Trans. Geosci.Remote Sens.,35, 1438-1445,
1997b.
ologydescribedhere shouldbecomefully exploitablewith the Gobron, N., B. Pinty, M. M. Verstraete, J. V. Martonchik, and D. J.
availability of instantaneousmultiangular and multispectral
Diner, The potentialof multiangularspectralmeasurements
to chardata.
acterizeland surfaces:Conceptualapproachand exploratoryapplication,J. Geophys.Res., 105, 17,539-17,550, 2000a.
Gobron, N., B. Pinty, M. M. Verstraete, and J.-L. Widlowski, Advancedspectralalgorithmand new vegetationindicesoptimizedfor
upcomingsensors:Development,accuracyand applications,IEEE
Acknowledgments. Some of the resultsshownin this paper were
presentedat the VEGETATION 2000 Symposium
at Belgirate,Italy, in
Trans. Geosci. Remote Sens., 36, 2489-2505, 2000b.
June2000.The authorsacknowledge
the supportof the VEGETATION
preparatoryprogramfinancedby France, the European Commission, Goel, N. S., and D. E. Strebel,Inversionof vegetationcanopyreflectance modelsfor estimatingagronomicvariables, 1, Problem defiBelgium, Italy, and Swedenand the inputs from V. Gond (Space
nition and initial resultsusingSuits model, RemoteSens.Environ.,
ApplicationsInstitute, Joint ResearchCentre, Italy) and D. L. Wil13, 487-507, 1983.
liams(BiosphericSciences
Branch,NASA GoddardSpaceFlightCenter, USA) who helped with their knowledgeof the variousconifer Goel, N. S., and W. Qin, Influencesof canopyarchitectureon relaspeciesin Europe and provided data on the optical properties of
tionshipsbetweenvariousvegetationindicesand LAI and FPAR: A
needle spectralreflectanceand transmittance.Part of this work has
computersimulation,RemoteSens.Rev., 10, 309-347, 1994.
been achieved in the framework of Jean-Luc Widlowski's Ph.D. thesis, Govaerts,Y., and M. M. Verstraete, Raytran: A Monte Carlo ray
who currentlybenefitsfrom a JRC doctoralfellowship.
tracingmodel to computelight scatteringin three-dimensionalheterogeneousmedia, IEEE Trans.Geosci.RemoteSens.,36, 493-505,
1998.
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(ReceivedDecember19, 2000;revisedJune22, 2001;
Pinty, B., F. Roveda, M. M. Verstraete, N. Gobron, Y. Govaerts, J. acceptedJuly 24, 2001.)